Notarization method for property checking

Abstract

The invention relates to the technical field of property management, in particular to a notarization method for property checking, which comprises the following steps: acquiring a checking number timestamp area and an article index to generate a responsibility chain, setting a responsibility index by sorting data, generating sequence data, generating fingerprints by Hash and binding numbers, and marking exceptions in overtime. And obtaining log comparison consistency and completion rate score classification risks, backtracking and calculating influence value attribution responsibilities, summarizing optimization responsibilities, and uploading the optimization responsibilities. According to the method, task sequence and timeliness of counting personnel are tracked through responsibility chain sorting, data are recorded, responsibility initialization indexes are set, data fingerprints are generated through a hash algorithm to guarantee data independence and completeness, time stamps are compared to mine time delay and mark abnormities, and log comparison consistency and completion rate are obtained based on an abnormal data set. Personalized risk assessment and personnel classification are generated, a responsibility chain sequence and a timestamp are backtracked to calculate a responsibility influence value, responsibility attribution analysis and optimization are formed, counting compliance and transparency are improved, and risks are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of property management, and in particular to a notarization method for property inventory. Background Art

[0002] The field of property management technology encompasses various technologies related to the identification, storage, evaluation, inventory, and transfer of property. Its core focus is on effectively managing property through various information technology tools, ensuring the accuracy, transparency, and security of property information. Property management technology is widely used by individuals, businesses, and governments, particularly in the management, transfer, and verification of high-value assets, demonstrating its importance and challenges. With the advancement of technology, particularly the maturity of information technology and the Internet of Things, property management is gradually moving towards intelligence, digitization, and automation. Research and practice in this field primarily focus on the establishment of digital property archives, asset information tracing, and inventory and verification methods.

[0003] Existing technologies for property inventorying present operational limitations, primarily in the traceability of inventory data and the lack of transparency regarding accountability. While notarization ensures the legal validity of inventory data, traditional methods rely on manual or periodic audits, which are prone to data delays and personnel oversight. This leads to delayed updates and a lack of real-time visibility into the completeness and accuracy of inventory data. Furthermore, the division of responsibilities among inventory personnel is unclear, lacking scientific indicators and data support. This often makes it difficult to accurately identify issues at specific stages, and when multiple personnel are involved, responsibility is unclear. More importantly, existing technologies often struggle to effectively process large amounts of data and ensure data consistency and timeliness when handling high-frequency, large-scale inventory tasks, significantly impacting the efficiency and accuracy of the inventory process. For example, if delays or errors occur at any stage during the asset inventory process, traditional methods struggle to detect and address them promptly, potentially leading to inconsistent information or lost records, creating unnecessary legal risks during asset transfers and dispute resolution. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a notarization method for property inventory.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a notarization method for property inventory, comprising the following steps: S1: Obtain the index of the inventory personnel number, timestamp, inventory area and item code to generate the responsibility chain index table, sort the inventory data submitted by the inventory personnel by upload time, set the responsibility initialization indicator, and generate the responsibility chain sequence data; S2: Based on the chain of responsibility sequence data, a data fingerprint is generated through a hash algorithm and bound to the inventory personnel number. If the timestamp exceeds the time interval threshold, the delayed verification status is marked to generate a time anomaly marking dataset; S3: Based on the time anomaly mark dataset, obtain the data logs of the corresponding inventory personnel and compare them to generate an operation consistency index. At the same time, obtain the task completion records to calculate the task completion rate. Use the fuzzy comprehensive evaluation model to perform risk scoring and classify the risk levels of the inventory personnel to generate an inventory risk classification result. S4: Based on the inventory risk classification results, items with inventory data changes are obtained, and backtracking is performed in combination with the responsibility chain sequence data and timestamps to calculate the responsibility impact value of the inventory personnel and perform responsibility attribution analysis to generate a total responsibility impact result; S5: Obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score of the inventory personnel, optimize the responsibility attribution of the inventory personnel based on the total responsibility impact result, and upload the optimized responsibility record result.

[0006] The responsibility chain sequence data includes the inventory personnel number index, timestamp sequence table, and responsibility chain area mapping value. The time anomaly marking data set specifically includes the delay marking status, data fingerprint hash value, and time interval comparison result. The inventory risk classification result includes the risk score interval, risk level label, and inventory personnel number grouping. The total responsibility impact result specifically refers to the responsibility impact value mapping table, inventory data change item number, and responsibility chain time sequence positioning information. The responsibility record result includes responsibility optimization number mapping, responsibility adjustment amount, and updated inventory personnel record table.

[0007] As a further solution of the present invention, the specific steps of S1 include: S101: Obtain the counting personnel number, counting timestamp, counting area code and item code index, sort the counting records in ascending order by the timestamp field, and map the sorted number index with the counting area code and item code to generate a counting sequence index. S102: Based on the count sequence index, identify the continuous submission intervals of each count person in the specified area, calibrate the data according to the start and end time of the interval, and calculate the count activity of the person in the area by using the timestamp length and number of records in the interval to obtain the person's count activity value; S103: Based on the personnel inventory activity value, the activity value is standardized by region to generate a responsibility value sequence, and the inventory responsibility chain data table is generated by combining the personnel number, region code and responsibility value sequence, and sorted in ascending order by the inventory timestamp to generate responsibility chain sequence data.

[0008] As a further solution of the present invention, the specific steps of S2 include: S201: Based on the responsibility chain sequence data, a hash algorithm is called to calculate a comprehensive feature value for the timestamp and the area code, and the hash value of each record is obtained as a data fingerprint. The data fingerprint is then bound to the corresponding personnel number to generate a responsibility chain data fingerprint; S202: Calling the responsibility chain data fingerprint, sequentially calculating the timestamp differences of consecutive records with the same number based on the inventory personnel number, determining whether the time interval exceeds the time interval threshold, marking the data records that meet the conditions, and obtaining the amount of candidate data for time anomaly; S203: Based on the amount of candidate time anomaly data, the identification set is reorganized according to the inventory personnel number and the record position, the original number and the regional field combination are called to construct a tag index table, the delayed identification and the original field sequence are merged, and the time anomaly tag data set is generated.

[0009] As a further solution of the present invention, the time interval threshold is set by performing a statistical distribution analysis on the timestamp differences of adjacent records in the inventory responsibility chain time series. First, the time difference sequence of continuous records under all inventory personnel numbers is collected, the mode and standard deviation are calculated, and then the 90% quantile of the entire difference sequence is combined for comparison and setting.

[0010] As a further solution of the present invention, the specific steps of S3 include: S301: Extract the corresponding inventory personnel number and time anomaly record index based on the time anomaly mark data set, and filter the operation behavior field and time field in the log, construct the operation behavior sequence and compare it item by item with the abnormal record behavior sequence, and calculate the operation consistency index; S302: Call the operation consistency index to obtain the total number of task completion records and the number of completed records for the corresponding inventory personnel, calculate the personnel's task completion ratio, normalize it with the operation consistency index value, calculate the comprehensive risk score using the fuzzy comprehensive evaluation model, and generate the inventory risk score; S303: Based on the risk inventory score, the set risk level classification standard value interval is called to determine the level interval position of the score value. The corresponding level label table is established in combination with the personnel number, and the risk level labeling and grouping processing are completed to generate the risk inventory classification result; The risk level classification standard value interval is set by performing cluster analysis on the risk scores of the inventory task data.

[0011] As a further solution of the present invention, the specific steps of S4 include: S401: Based on the inventory risk classification result, all inventory item records and inventory time information corresponding to the personnel number are obtained, and record entries with changes in the item status field are screened. The corresponding item number and status change time and location interval are extracted, and the item status change ratio is calculated to obtain the inventory item change rate; S402: Based on the change rate of the counted items, the responsibility chain sequence data and timestamp of the corresponding item number are extracted. The behavior path and operation sequence of the inventory personnel number are combined, and the responsibility score of each responsibility chain is calculated based on the frequency of occurrence of the personnel in the responsibility chain and the corresponding operation interval to obtain the inventory responsibility score combination; S403: Based on the inventory responsibility score combination, the scores of the same inventory personnel in multiple responsibility chains are directly accumulated, a corresponding mapping table between the inventory personnel number and the accumulated score is established, and the mapping table is sorted and organized according to the personnel number to generate a total responsibility impact result.

[0012] As a further solution of the present invention, the specific steps of S5 include: S501: Based on the total responsibility impact result, obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score and match them according to the inventory personnel number, extract all timestamp and data fingerprint information under the same number and perform sequence aggregation processing to generate an inventory data sequence set; S502: Calling the inventory data sequence set, performing difference determination on the data fingerprints at multiple time points in the sequence, combining the corresponding responsibility impact values ​​and risk scores, classifying and marking the records that exceed the responsibility attribution adjustment threshold, sorting and mapping them according to the number of markings and the impact value, and obtaining the attribution determination adjustment amount; S503: According to the attribution determination adjustment amount, the responsibility mapping relationship of all inventory personnel numbers in the original responsibility impact result is optimized and recorded and packaged, and the optimized responsibility record result is uploaded.

[0013] As a further solution of the present invention, the responsibility attribution adjustment threshold is determined by collecting and standardizing the data of the inventory personnel, including inventory time, item change records, risk scores and responsibility chain information, and determining the responsibility scope and risk fluctuation setting of each inventory personnel through risk impact.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by sorting the responsibility chain of the indexes of the inventory personnel number, timestamp, inventory area and item code, it is possible to effectively track the order and timeliness of task submission of each inventory personnel, while accurately recording the inventory data and setting the responsibility initialization index to ensure clear and accurate responsibility allocation. Combined with the hash algorithm to generate data fingerprints and bind them, it is possible to ensure the independence and integrity of each inventory data. By comparing the timestamps, delay problems can be discovered and anomalies can be marked in a timely manner, providing effective early warning for any emergencies in the inventory process. In addition, based on the time anomaly marking data set, the data logs of the inventory personnel are further obtained and compared for consistency, providing accurate quantitative indicators for the completion of the inventory task, thereby generating personalized risk assessments and personnel classifications. This risk classification-based processing logic can identify potential problems at the earliest stage and avoid risk transfer caused by data deviations. By tracing back the responsibility chain sequence data and timestamps, the responsibility impact value of the inventory personnel can be calculated in detail, forming a comprehensive responsibility attribution analysis, providing data support for responsibility attribution optimization, and avoiding the opacity in the traditional inventory process. Ultimately, by optimizing the upload of responsibility records, the compliance and transparency of the inventory process were improved, effectively avoiding risks caused by human omissions or data errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the main steps of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: a notarization method for property inventory, comprising the following steps: S1: Obtain the index of the inventory personnel number, timestamp, inventory area and item code to generate the responsibility chain index table, sort the inventory data submitted by the inventory personnel by upload time, set the responsibility initialization indicator, and generate the responsibility chain sequence data; S2: Based on the chain of responsibility sequence data, a data fingerprint is generated through a hash algorithm and bound to the inventory personnel number. If the timestamp exceeds the time interval threshold, the delayed verification status is marked to generate a time anomaly marking dataset; S3: Based on the time anomaly tagging dataset, the data logs of the corresponding inventory personnel are obtained and compared to generate the operation consistency index. At the same time, the task completion records are obtained to calculate the task completion rate. The risk scores are scored and the risk levels of the inventory personnel are classified using the fuzzy comprehensive evaluation model to generate the inventory risk classification results. S4: Based on the inventory risk classification results, obtain items with inventory data changes, combine the responsibility chain sequence data and timestamps to perform backtracking, calculate the responsibility impact value of the inventory personnel, conduct responsibility attribution analysis, and generate the total responsibility impact result; S5: Obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score of the inventory personnel, optimize the responsibility attribution of the inventory personnel based on the total responsibility impact result, and upload the optimized responsibility record results.

[0019] The responsibility chain sequence data includes the inventory personnel number index, timestamp sequence table, and responsibility chain area mapping value. The time anomaly mark data set specifically includes the delay mark status, data fingerprint hash value, and time interval comparison result. The inventory risk classification results include risk score interval, risk level label, and inventory personnel number grouping. The total responsibility impact result specifically refers to the responsibility impact value mapping table, inventory data change item number, and responsibility chain time sequence positioning information. The responsibility record upload result includes responsibility optimization number mapping, responsibility adjustment amount, and updated inventory personnel record table.

[0020] See also Figure 1 , the specific steps for obtaining S1 are: S101: Obtain the counting personnel number, counting timestamp, counting area code and item code index, sort the counting records in ascending order by the timestamp field, and map the sorted number index with the counting area code and item code to generate a counting sequence index. Get the inventory personnel number, inventory timestamp, inventory area code and item code index, query the inventory record table from the database, and extract the field values, including the personnel number P001 and the timestamp 2023-01-01 09:00:00, area code is Z01, item code is I001, compare the timestamp values, swap the record positions and arrange in ascending order, including three records with timestamps 09:00:00, 09:05:00, and 08:55:00. After comparison, they are adjusted to 08:55:00, 09:00:00, and 09:05:00. Take the index position of the sorted record, the index starts at 0 and increases, index 0 corresponds to the first record, index 1 corresponds to the second record, and index 2 corresponds to the third record. Call the index value to associate the area code and item code to generate a mapping sequence. Each entry includes the index value, personnel number, area code, and item code, including index 0 mapped to (P001, Z01, I001), index 1 mapped to (P001, Z01, I002), and index 2 mapped to (P002, Z01, I003), forming the inventory sequence index quantity.

[0021] Table 1: Example of inventory record

[0022] As shown in Table 1, the original inventory record data is listed. The time stamp unit is seconds. The number of records is 4. The sequence before sorting is record 1 timestamp 09:00:00, record 2 timestamp 09:05:00, record 3 timestamp 09:15:00, record 4 timestamp 08:55:00. Comparing the timestamp values, record 4 timestamp 08:55:00 is less than record 1 timestamp 09:00:00. The positions of record 4 and record 1 are swapped. The timestamp 09:00:00 of record 1 is less than the timestamp 09:05:00 of record 2, and the position remains unchanged. The timestamp 09:05:00 of record 2 is less than the timestamp 09:15:00 of record 3. The sequence after sorting is unchanged. The timestamp of record 4 is 08:55:00, the timestamp of record 1 is 09:00:00, the timestamp of record 2 is 09:05:00, and the timestamp of record 3 is 09:15:00. The index value is assigned to index 0 corresponding to record 4, index 1 corresponding to record 1, index 2 corresponding to record 2, and index 3 corresponding to record 3. The index value 0 is called to associate the personnel number P001, the area code Z01, and the item code I004. The index value 1 is associated with P001, Z01, and I001. The index value 2 is associated with P001, Z01, and I002. The index value 3 is associated with P002, Z01, and I003. The index quantity of the inventory sequence is generated as a list [(0, P001, Z01, I004), (1, P001, Z01, I001), (2, P001, Z01, I002), (3, P002, Z01, I003)].

[0023] S102: Based on the count sequence index, identify the continuous submission intervals of each count person in the specified area, calibrate the data according to the interval start and end time, and use the timestamp length and number of records in the interval to calculate the count activity of the person in the area, and obtain the person count activity value; Based on the inventory sequence index, take the timestamp, personnel number, and area code fields in the sequence index. For each combination of personnel number and area code, traverse the timestamp sequence and calculate the difference between adjacent timestamps. The difference unit is seconds. Set the continuous submission time threshold to 30 seconds. The threshold setting is based on the average interval of the current inventory operation, including the average interval of 20 seconds for monitoring data. Set the threshold to 30 seconds to tolerate delays. Determine whether the difference is less than or equal to 30 seconds. If the difference is less than or equal to 30 seconds, merge them into the same interval. Otherwise, end the current interval and Start a new interval, record the interval start time, end time, and number of records, including the timestamp sequence of person P001 area Z01 08:55:00, 09:00:00, 09:05:00, calculate the time difference 09:00:00 minus 08:55:00 to get 300 seconds, determine if 300 seconds is greater than 30 seconds, end the first interval start 08:55:00 end 08:55:00 record number 1, start a new interval start 09:00:00, calculate the next difference 09:05:00 minus 09:00:00 0 equals 300 seconds, and 300 seconds is greater than 30 seconds. The second interval ends at 09:00:00 and ends at 09:00:00, with a record count of 1. The third interval begins at 09:05:00 and ends at 09:05:00, with a record count of 1. The interval list is marked, and the interval start and end times are called. The timestamp length is calculated as the end time minus the start time plus 1 second (to avoid zero length). The number of records is obtained, and the count activity is calculated as the number of records divided by the timestamp length, including the interval start 09:05:00 and end 09:1 The timestamp length is 1 second, the number of records is 1, and the activity level is 1 divided by 1 to obtain 1.0 records per second. The interval starts at 09:15:00 and ends at 09:20:00. The timestamp length is 1 second, the number of records is 1, and the activity level is 1 divided by 1 to obtain 1.0 records per second. Define high and low activity intervals. Based on the average activity value of 0.5 records per second for all intervals, set low activity to less than 0.4, medium activity to 0.4 to 0.6, and high activity to greater than 0.6. Activity levels greater than 1.0 are considered high activity, and obtain the activity value of personnel counts.

[0024] S103: Based on the personnel inventory activity values, the activity values ​​are standardized by region to generate a responsibility value sequence. The inventory responsibility chain data table is generated by combining the personnel number, region code, and responsibility value sequence. The table is sorted in ascending order by inventory timestamp to generate responsibility chain sequence data. According to the personnel inventory activity value, take the activity value list, for each area code, calculate the average activity of all personnel in the area, the average value is equal to the sum of the activity divided by the number of personnel, including area Z01 personnel P001 activity 1.0, P002 activity 0.5, the sum 1.5 divided by the number of personnel 2 to get the average value 0.75, the standardized responsibility value is equal to the individual activity divided by the regional average activity, including P001 responsibility value 1.0 divided by 0.75 is about 1.333, P002 responsibility value 0.5 divided by 0.75 is about 0.667, generate the responsibility value sequence, combine the personnel number, area code and responsibility value sequence, generate the data table fields personnel number, area code, responsibility value, including rows P001 Z01 1.333, P002 Z01 0.667, sort in ascending order of inventory timestamp, take the timestamp field in the inventory sequence index, sort the data table timestamps from small to large, including P001 timestamp 09:00:00 and P002 timestamp 09:15:00, the sorted sequence P001 responsibility value is 1.333, P002 responsibility value is 0.667, and the responsibility chain sequence data is generated.

[0025] See also Figure 1 , the specific steps for obtaining S2 are: S201: Based on the responsibility chain sequence data, a hash algorithm is used to calculate a comprehensive feature value for the timestamp and region code, and the hash value of each record is obtained as a data fingerprint. The data fingerprint is then bound to the corresponding personnel number to generate a responsibility chain data fingerprint. Based on the chain of responsibility sequence data, extract the timestamp field and area code field in the record, Represents the comprehensive feature value, which is used to generate the intermediate value of the data fingerprint. Represents the timestamp value (unit represents seconds), Unix timestamp format, Represents the time offset (unit represents seconds), the base time adjustment item, Represents the timestamp weight factor (dimensionless), which adjusts the contribution of the timestamp. represents the time component scaling factor (dimensionless), controlling the magnitude of the time square root term, Represents the regional coding adjustment parameters to balance the impact of regional coding. Represents the area code value (dimensionless), an integer mapped by a string, the acquisition process, : Get the record timestamp 2023-01-01, 09:00:00 by actually counting the records and convert it to Unix timestamp: Second, :Region coding quantization, establish coding dictionary, region coding Z01 mapping is , : Based on data analysis, statistics of 1000 records, the timestamp variance accounts for 85% of the total feature variance, so , :System base time setting, system base time 2023-01-01, 00:00:00, Second, : Calculated through the test data set, taking 100 test records The average value is ≈40900, and the target range is 20000, so , : Regional influence weight analysis, regional coding variance accounts for 8% of the total feature variance, so we take Balance the impact, substitute into the formula , : , , , ,result As input, a data fingerprint (including e3b0c44298fc1c14) is generated through SHA-256 hash operation, and the personnel number such as P001 is bound to generate a data fingerprint entry. This process is repeated for each record to form a chain of responsibility data fingerprint.

[0026] S202: Call the responsibility chain data fingerprint, calculate the timestamp difference of consecutive records with the same number in sequence according to the inventory personnel number, determine whether the time interval exceeds the time interval threshold, mark the data records that meet the conditions, and obtain the amount of candidate data for time anomaly; Call the responsibility chain data fingerprint list, extract the personnel number field and the timestamp field corresponding to the data fingerprint. The timestamp unit is seconds. Sort the records under the same personnel number in ascending order by timestamp, including the timestamp sequence 1672531200, 1672531300, and 1672531600 of personnel P001 records. Calculate the difference between adjacent timestamps. The difference is equal to the timestamp of the next record minus the timestamp of the previous record, including difference 1: 1672531300 minus 1672531200 = 100 seconds, difference 2: 1672531600 minus 1672531300 = 300 seconds. Set the time interval threshold to 300 seconds. The threshold refers to the average interval of the current inventory operation, and the average data interval is 2 50 seconds, set 300 seconds to tolerate reasonable delays, determine whether the difference is greater than 300 seconds, if the difference is greater than 300 seconds, mark the interval point as an anomaly, including not marking when the difference is 100 seconds less than or equal to 300 seconds, and not marking when the difference is 300 seconds or equal to 300 seconds. However, if the threshold is set to be strictly greater than 300 seconds to mark, then the difference of 300 seconds will not be marked. If the difference between the record timestamps 1672531200 and 1672531600 is 400 seconds, it is determined that 400 seconds is greater than 300 seconds, and the latter record is marked as an anomaly. Obtain the marking results, including that the difference between the record sequence index 1 and index 2 is 400 seconds exceeding the threshold, and the record index 2 is marked as an anomaly. Repeat this process for all personnel numbers to generate a list of candidate data for time anomaly.

[0027] S203: Based on the amount of candidate time anomaly data, the identification set is reorganized according to the inventory personnel number and the record location, the original number and the region field combination are called to build a tag index table, and the delayed identification and the original field sequence are merged to generate a time anomaly tag data set; According to the list of candidate data for temporal anomaly, the marked abnormal record index, personnel number, and area code fields are extracted. The data are grouped by personnel number. The records in each group are sorted by their original position, including the abnormal record index 2 of personnel P001. The data are reorganized into the identification set entry (P001, index 2). The personnel number and area code fields in the original responsibility chain sequence data are called to construct a combined marking index table. The fields include personnel number, area code, and abnormal mark, including the entry (P001, Z01, 1) to represent abnormalities. Combined with the original timestamp field, the delayed mark is merged with the original field sequence, including the original record (P001, Z01, 1672531200). The abnormal mark field 0 or 1 is added, and mark 1 represents an abnormality. New sequence entries (P001, Z01, 1672531200, 0) and (P001, Z01, 1672531600, 1) are generated. All records are processed to generate a temporal anomaly marking dataset.

[0028] See also Figure 1 , the specific steps for obtaining S3 are: S301: Extract the corresponding inventory personnel number and time anomaly record index based on the time anomaly mark dataset, and filter the operation behavior field and time field in the log to construct the operation behavior sequence and compare it item by item with the abnormal record behavior sequence to calculate the operation consistency index; Based on the time anomaly tag dataset, we extracted personnel number P001 and time anomaly record index 2. We screened the operation behavior fields (such as "scan", "input", "submit") and time fields (such as 1672531200) corresponding to the index in the operation log. We constructed a normal operation behavior sequence ["scan", "input", "submit"] and an abnormal record behavior sequence ["scan", "skip", "submit"]. We compared the behaviors at the same index position item by item. The behaviors at index 0 were the same (scan = scan), the behaviors at index 1 were different (input ≠ skip), and the behaviors at index 2 were the same (submit = submit). We counted the number of identical items (2) and the total number of items (3), and calculated the consistency ratio. , and obtain the operation consistency index value of 0.6667.

[0029] S302: Call the operation consistency index to obtain the total number of task completion records and the number of completed records for the corresponding inventory personnel, calculate the personnel's task completion ratio, normalize it with the operation consistency index value, calculate the comprehensive risk score using the fuzzy comprehensive evaluation model, and generate the inventory risk score; The call operation consistency index value is 0.6667. (Task completion ratio), acquisition process: Count the number of records completed by the inventory personnel (8) and the total number of tasks (10), and calculate the ratio , (Normalized operation consistency index) comes from the behavioral sequence alignment results. The normal sequence ["scan", "input", "submit"] is aligned with the abnormal sequence ["scan", "skip", "submit"]. The identical items 2 / 3 ≈ 0.6667. (Environmental complexity factor), collect regional safety rating data (1-10 points, 10 is the safest), mapping formula: , Zone Z01 safety rating 7 points → , (scaling factor), analyzing 1000 risk scores, the maximum value is 95, rounded Control the output range and substitute it into the formula to calculate the comprehensive risk score. , , , , , , , ,result If it is less than the low risk threshold of 0.01, it means that the current inventory risk is controllable and is directly output as the inventory risk score.

[0030] S303: Based on the risk inventory score, the risk level classification standard value interval is called to determine the level interval position of the score value. The corresponding level label table is established based on the personnel number. The risk level labeling and grouping processing are completed to generate the risk inventory classification result. The standard value interval for risk level classification is set by clustering analysis of the risk scores of inventory task data; According to the inventory risk score of 0.008675, the risk level classification standard is called: low risk (0, 0.01), medium risk (0.01, 0.05), and high risk [0.05, 1] ​​(the standard is generated by K-means clustering of 1000 risk scores, with cluster centers of 0.005, 0.03, and 0.08). It is judged that 0.008675∈(0, 0.01) is low risk, and the risk level label of person number P001 is marked as low risk. The same process is applied to other people (for example, P002 with a score of 0.032 is marked as medium risk), generating the risk level label table shown in Table 2.

[0031] Table 2 Risk level label table

[0032] The patients were grouped and classified into low-risk group [P001] and medium-risk group [P002] to generate the risk classification results.

[0033] See also Figure 1 , the specific steps for obtaining S4 are: S401: Based on the inventory risk classification results, all inventory item records and inventory time information corresponding to the personnel number are obtained, and records with changes in the item status field are screened. The corresponding item numbers and status change time intervals are extracted, and the percentage of item status changes is calculated to obtain the inventory item change rate. Based on the inventory risk classification results (e.g., low-risk individual P001), all 10 inventory records for P001 were extracted. Each record contained an item code, timestamp, and item status field (e.g., normal, damaged). Status change records were screened: record 2 (normal at 09:05 → damaged at 09:10) and record 5 (normal at 09:25 → missing at 09:30). Item codes I002 and I005, as well as the change time intervals [09:05, 09:10] and [09:25, 09:30], were extracted. The change percentage was calculated as follows: the number of change records (2) divided by the total number of records (10) equals 0.2, resulting in a change rate of 20% for the counted items. The status change determination criterion is: if the status values ​​of adjacent timestamps are different, it is counted as a change. The time interval is the start and end timestamps of the change records.

[0034] Table 3: Item Status Change Record

[0035] Table 3 shows an example of state change records. The timestamp unit is accurate to seconds, and the change rate is calculated by dividing the number of change records (2) by the total number of records (10).

[0036] S402: Based on the change rate of the counted items, the responsibility chain sequence data and timestamp of the corresponding item number are extracted. The behavior path and operation sequence of the inventory personnel number are combined, and the responsibility score of each responsibility chain is calculated based on the frequency of occurrence of the personnel in the responsibility chain and the corresponding operation interval to obtain the inventory responsibility score combination; Based on a 20% inventory item turnover rate, extract the responsibility chain sequence data for item I002 (personnel P001, area Z01, responsibility value 1.333), timestamp 09:05, and obtain P001's behavior path from 09:00 to 09:10: the operation sequence [pick up, scan, place], operation interval: 3 seconds from pick up to scan, and 2 seconds from scan to place. Calculate the single responsibility chain responsibility score: multiply the occurrence frequency 1 by the inverse of the average operation interval (1 / 3 + 1 / 2) / 2 ≈ 0.4167, to obtain a responsibility score of 0.4167. Similarly, calculate I005's responsibility score 0.5833 (operation interval, [pick up, scan] 1 second → inverse 1), and generate the inventory responsibility score combination [I002: 0.4167, I005: 0.5833]. The operation interval is calculated as the time difference between the start and end of the action.

[0037] S403: Based on the inventory responsibility score combination, the scores of the same inventory personnel in multiple responsibility chains are directly accumulated, and a mapping table is established between the inventory personnel number and the accumulated scores. The mapping table is sorted and organized according to the personnel number to generate the total responsibility impact result; Based on the inventory responsibility score combination P001: [I002: 0.4167, I005: 0.5833); P002: [I003: 0.75, I007: 0.75]; P003: [I004: 1.2]; P004: [I006: 0.9, I008: 0.6, I009: 0.5], the cumulative scores of multiple items for the same person are: P001 total score = 0.4167 + 0.5833 = 1.0, P002 total score = 0.75 + 0.75 = 1.5, P003 total score = 1.2, P004 total score = 0.9 + 0.6 + 0.5 = 2.0, and the mapping table shown in Table 4 is established: Table 4 Mapping table:

[0038] Sort by personnel number in ascending order (P001 → P002 → P003 → P004) to generate the total responsibility impact result. The risk level is generated previously, and the number of items responsible is calculated by counting the number of items in the responsibility score combination. The cumulative score is the direct addition of the responsibility scores of multiple items.

[0039] See also Figure 1 , the specific steps for obtaining S5 are: S501: Based on the total responsibility impact result, obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score and match them according to the inventory personnel number. Under the same number, extract all timestamps and data fingerprint information and perform sequence aggregation processing to generate an inventory data sequence set; Based on the total responsibility impact result (personnel P001's cumulative score is 1.0, and P002's cumulative score is 1.5), all P001 records are extracted: timestamp sequence [1672531200, 1672531300], data fingerprint sequence ["e3b0c442", "a1b2c3d4"], responsibility impact value 1.0, risk score 0.008435, and matched and grouped by personnel number to generate the inventory data sequence set: P001 group {timestamp sequence [1672531200, 1672531300], data fingerprint sequence ["e3b0c442", "a1b2c3d4"], responsibility impact value 1.0, risk score 0.008435}, where the data fingerprint is derived from the SHA-256 hash value, and the timestamp is in seconds. The sequence grouping method is to group by number and then merge the timestamp and data fingerprint into an array.

[0040] S502: Call the inventory data sequence set, perform difference determination on the data fingerprints at multiple time points in the sequence, combine the corresponding responsibility impact values ​​and risk scores, classify and mark the records that exceed the responsibility attribution adjustment threshold, sort and map them according to the number of markings and impact values, and obtain the attribution determination adjustment amount; Call P001 to count the data sequence set and calculate the difference in adjacent data fingerprints: Convert the hexadecimal fingerprint to a decimal integer, "e3b0c442" → 3,814,749,762, "a1b2c3d4" → 2,714,674,132. The absolute value of the difference |3,814,749,762 - 2,714,674,132| = 1,100,075,630. Combined with the responsibility impact value of 1.0 and the risk score of 0.008435, calculate the comprehensive difference value: 1,100,075,630 × 1.0 × 0.008435 = 9,279,137. Set the responsibility attribution adjustment. The threshold is 10,000,000 (95% percentile of difference value, based on statistics of 1,000 records: maximum value 15,000,000, minimum value 500,000, 95% percentile value 10,000,000). If 9,279,137 < 10,000,000, it is not marked. If the difference value of P002 record is 12,000,000 > 10,000,000, it is marked. The number of markings is counted (for example, P002 is marked once), and the order is based on the number of markings × responsibility impact value: P002 (1 × 1.5 = 1.5) > P001 (0 × 1.0 = 0). The difference judgment parameter table shown in Table 5 is established.

[0041] Table 5 Difference judgment parameters

[0042] S503: Based on the attribution determination adjustment amount, the responsibility mapping relationship of all inventory personnel numbers in the original responsibility impact results is optimized and packaged, and the optimized responsibility record results are uploaded; According to the attribution judgment adjustment scale (P002 adjustment amount 1.5, P001 adjustment amount 0), optimize the original responsibility impact result (P001 original value 1.0, P002 original value 1.5): P002 new value = original value + adjustment amount = 1.5 + 1.5 = 3.0, P001 new value = 1.0 + 0 = 1.0, establish an optimization mapping table, package the record format: {personnel number: "P001", optimized responsibility value: 1.0, timestamp: "2023-01-01"}, and upload it to the database responsibility record table.

[0043] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A notarization method for property inventory, characterized in that: The following steps are involved: S1: Obtain the index of the inventory personnel number, timestamp, inventory area and item code to generate the responsibility chain index table, sort the inventory data submitted by the inventory personnel by upload time, set the responsibility initialization indicator, and generate the responsibility chain sequence data; S2: Based on the chain of responsibility sequence data, a data fingerprint is generated through a hash algorithm and bound to the inventory personnel number. If the timestamp exceeds the time interval threshold, the delayed verification status is marked to generate a time anomaly marking dataset; S3: Based on the time anomaly mark dataset, obtain the data logs of the corresponding inventory personnel and compare them to generate an operation consistency index. At the same time, obtain the task completion records to calculate the task completion rate. Use the fuzzy comprehensive evaluation model to perform risk scoring and classify the risk levels of the inventory personnel to generate an inventory risk classification result. S4: Based on the inventory risk classification result, obtain items with inventory data changes, combine the responsibility chain sequence data and timestamp to backtrack, calculate the responsibility impact value of the inventory personnel and perform responsibility attribution analysis to generate a total responsibility impact result.

2. The notarization method for property inventory according to claim 1, characterized in that: The responsibility chain sequence data includes the inventory personnel number index, timestamp sequence table, and responsibility chain area mapping value. The time anomaly marking data set specifically includes the delay marking status, data fingerprint hash value, and time interval comparison result. The inventory risk classification result includes the risk score interval, risk level label, and inventory personnel number grouping. The total responsibility impact result specifically refers to the responsibility impact value mapping table, inventory data change item number, and responsibility chain timing positioning information.

3. The notarization method for property inventory according to claim 1, characterized in that: The specific steps of S1 include: S101: Obtain the counting personnel number, counting timestamp, counting area code and item code index, sort the counting records in ascending order by the timestamp field, and map the sorted number index with the counting area code and item code to generate a counting sequence index. S102: Based on the count sequence index, identify the continuous submission intervals of each count person in the specified area, calibrate the data according to the start and end time of the interval, and calculate the count activity of the person in the area by using the timestamp length and number of records in the interval to obtain the person's count activity value; S103: Based on the personnel inventory activity value, the activity value is standardized by region to generate a responsibility value sequence, and the inventory responsibility chain data table is generated by combining the personnel number, region code and responsibility value sequence, and sorted in ascending order by the inventory timestamp to generate responsibility chain sequence data.

4. The notarization method for property inventory according to claim 1, characterized in that: The specific steps of S2 include: S201: Based on the responsibility chain sequence data, a hash algorithm is called to calculate a comprehensive feature value for the timestamp and the area code, and the hash value of each record is obtained as a data fingerprint. The data fingerprint is then bound to the corresponding personnel number to generate a responsibility chain data fingerprint; S202: Calling the responsibility chain data fingerprint, sequentially calculating the timestamp differences of consecutive records with the same number based on the inventory personnel number, determining whether the time interval exceeds the time interval threshold, marking the data records that meet the conditions, and obtaining the amount of candidate data for time anomaly; S203: Based on the amount of candidate time anomaly data, the identification set is reorganized according to the inventory personnel number and the record position, the original number and the regional field combination are called to construct a tag index table, the delayed identification and the original field sequence are merged, and the time anomaly tag data set is generated.

5. The notarization method for property inventory according to claim 4, characterized in that: The time interval threshold is set by performing a statistical distribution analysis on the timestamp differences of adjacent records in the inventory responsibility chain time series. First, the time difference sequence of consecutive records under all inventory personnel numbers is collected, the mode and standard deviation are calculated, and then the 90% quantile of the entire difference sequence is used for comparison and setting.

6. The notarization method for property inventory according to claim 1, characterized in that: The specific steps of S3 include: S301: Extract the corresponding inventory personnel number and time anomaly record index based on the time anomaly mark data set, and filter the operation behavior field and time field in the log, construct the operation behavior sequence and compare it item by item with the abnormal record behavior sequence, and calculate the operation consistency index; S302: Call the operation consistency index to obtain the total number of task completion records and the number of completed records for the corresponding inventory personnel, calculate the personnel's task completion ratio, normalize it with the operation consistency index value, calculate the comprehensive risk score using the fuzzy comprehensive evaluation model, and generate the inventory risk score; S303: Based on the risk inventory score, the set risk level classification standard value interval is called to determine the level interval position of the score value. The corresponding level label table is established in combination with the personnel number, and the risk level labeling and grouping processing are completed to generate the risk inventory classification result; The risk level classification standard value interval is set by performing cluster analysis on the risk scores of the inventory task data.

7. The notarization method for property inventory according to claim 1, characterized in that: The specific steps of S4 include: S401: Based on the inventory risk classification result, all inventory item records and inventory time information corresponding to the personnel number are obtained, and record entries with changes in the item status field are screened. The corresponding item number and status change time and location interval are extracted, and the item status change ratio is calculated to obtain the inventory item change rate; S402: Based on the change rate of the counted items, the responsibility chain sequence data and timestamp of the corresponding item number are extracted. The behavior path and operation sequence of the inventory personnel number are combined, and the responsibility score of each responsibility chain is calculated based on the frequency of occurrence of the personnel in the responsibility chain and the corresponding operation interval to obtain the inventory responsibility score combination; S403: Based on the inventory responsibility score combination, the scores of the same inventory personnel in multiple responsibility chains are directly accumulated, a corresponding mapping table between the inventory personnel number and the accumulated score is established, and the mapping table is sorted and organized according to the personnel number to generate a total responsibility impact result.

8. The notarization method for property inventory according to claim 1, characterized in that: The method further comprises: S5: Obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score of the inventory personnel, optimize the responsibility attribution of the inventory personnel based on the total responsibility impact result, and upload the optimized responsibility record result; The responsibility record results include responsibility optimization number mapping, responsibility adjustment amount, and updated inventory personnel record table.

9. The notarization method for property inventory according to claim 8, characterized in that: The specific steps of S5 include: S501: Based on the total responsibility impact result, obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score and match them according to the inventory personnel number, extract all timestamp and data fingerprint information under the same number and perform sequence aggregation processing to generate an inventory data sequence set; S502: Calling the inventory data sequence set, performing difference determination on the data fingerprints at multiple time points in the sequence, combining the corresponding responsibility impact values ​​and risk scores, classifying and marking the records that exceed the responsibility attribution adjustment threshold, sorting and mapping them according to the number of markings and the impact value, and obtaining the attribution determination adjustment amount; S503: According to the attribution determination adjustment amount, the responsibility mapping relationship of all inventory personnel numbers in the original responsibility impact result is optimized and recorded and packaged, and the optimized responsibility record result is uploaded.

10. The notarization method for property inventory according to claim 9, characterized in that: The responsibility attribution adjustment threshold is determined by collecting and standardizing the data of inventory personnel, including inventory time, item change records, risk scores and responsibility chain information, and determining the responsibility scope and risk fluctuation setting of each inventory personnel through risk impact.

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